Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0518.202404
Jiang Fan, Junwei Qin, Liu Lei, Tian Hui
The Internet of Medical Things (IoMT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, IoMT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices (UDs) considering device-to-device (D2D) communication and a multi-access edge computing (MEC) technique under the scenario of IoMT. Specifically, to minimize the total delay and energy consumption concerning the requirement of IoMT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks' offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning (DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading (DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cache-aided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε — greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the IoMT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.
在可预见的 6G 时代，医疗物联网（IoMT）被视为智能医疗的关键技术。然而，由于边缘设备的计算能力有限以及与任务相关的耦合关系，IoMT 面临着前所未有的挑战。考虑到任务之间的关联关系，本文提出了一种考虑到设备到设备（D2D）通信的多用户设备（UDs）计算卸载策略，以及 IoMT 场景下的多访问边缘计算（MEC）技术。具体来说，为了最大限度地减少 IoMT 要求的总延迟和能耗，我们首先分析和模拟了详细的本地执行、MEC 执行、D2D 执行和相关任务卸载交换模型。因此，多 UD 的关联任务卸载方案被表述为一个混合整数非凸优化问题。考虑到深度强化学习（DRL）在处理耦合关系相关任务方面的优势，提出了一种基于双DQN的关联任务计算卸载（DDATO）算法，以获得最优解，从而在UD任务具有关联性的条件下做出最佳卸载决策。此外，为了降低 DDATO 算法的复杂性，有意在数据训练过程之前引入了缓存辅助程序。这就避免了多余的卸载和计算程序，而这些程序涉及的任务之前已被其他 UD 缓存。此外，我们在算法的行动选择部分使用了动态ε-贪婪策略，从而防止算法陷入局部最优解。仿真结果表明，与针对 IoMT 网络中不同结构的关联任务模型的其他现有方法相比，所提出的算法能更有效、更高效地降低总成本，同时还能在延迟和能耗容忍度之间进行权衡。
{"title":"Associative tasks computing offloading scheme in Internet of medical things with deep reinforcement learning","authors":"Jiang Fan, Junwei Qin, Liu Lei, Tian Hui","doi":"10.23919/JCC.fa.2023-0518.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0518.202404","url":null,"abstract":"The Internet of Medical Things (IoMT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, IoMT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices (UDs) considering device-to-device (D2D) communication and a multi-access edge computing (MEC) technique under the scenario of IoMT. Specifically, to minimize the total delay and energy consumption concerning the requirement of IoMT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks' offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning (DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading (DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cache-aided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε — greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the IoMT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140781608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0489.202404
Tianhao Lin, Zhiyong Luo
The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks, enabling global coverage and offering users ubiquitous computing power support, which is an important development direction of future communications. In this paper, we take into account a multi-scenario network model under the coverage of low earth orbit (LEO) satellite, which can provide computing resources to users in faraway areas to improve task processing efficiency. However, LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex, which makes the extraction of state information a daunting task. Therefore, we explore the dynamic resource management issue pertaining to joint computing, communication resource allocation and power control for multi-access edge computing (MEC). In order to tackle this formidable issue, we undertake the task of transforming the issue into a Markov decision process (MDP) problem and propose the self-attention based dynamic resource management (SABDRM) algorithm, which effectively extracts state information features to enhance the training process. Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.
{"title":"A self-attention based dynamic resource management for satellite-terrestrial networks","authors":"Tianhao Lin, Zhiyong Luo","doi":"10.23919/JCC.fa.2023-0489.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0489.202404","url":null,"abstract":"The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks, enabling global coverage and offering users ubiquitous computing power support, which is an important development direction of future communications. In this paper, we take into account a multi-scenario network model under the coverage of low earth orbit (LEO) satellite, which can provide computing resources to users in faraway areas to improve task processing efficiency. However, LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex, which makes the extraction of state information a daunting task. Therefore, we explore the dynamic resource management issue pertaining to joint computing, communication resource allocation and power control for multi-access edge computing (MEC). In order to tackle this formidable issue, we undertake the task of transforming the issue into a Markov decision process (MDP) problem and propose the self-attention based dynamic resource management (SABDRM) algorithm, which effectively extracts state information features to enhance the training process. Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140788811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0480.202404
Yang Ying, Lidong Zhu, Changjie Cao
In recent years, deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment. However, training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs. This paper proposes a support databased core-set selection method (SD) for signal recognition, aiming to screen a representative subset that approximates the large signal dataset. Specifically, this subset can be identified by employing the labeled information during the early stages of model training, as some training samples are labeled as supporting data frequently. This support data is crucial for model training and can be found using a border sample selector. Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size, and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset. The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.
{"title":"A support data-based core-set selection method for signal recognition","authors":"Yang Ying, Lidong Zhu, Changjie Cao","doi":"10.23919/JCC.fa.2023-0480.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0480.202404","url":null,"abstract":"In recent years, deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment. However, training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs. This paper proposes a support databased core-set selection method (SD) for signal recognition, aiming to screen a representative subset that approximates the large signal dataset. Specifically, this subset can be identified by employing the labeled information during the early stages of model training, as some training samples are labeled as supporting data frequently. This support data is crucial for model training and can be found using a border sample selector. Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size, and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset. The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140760758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0488.202404
Xiaoge Huang, Hongbo Yin, Cao Bin, Yongsheng Wang, Qianbin Chen, Zhang Jie
Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things (IoT) devices. To ensure the security of private data, in this paper, we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network. A reputation model is proposed to update the credibility of the fog nodes (FN), which is used to select blockchain nodes (BN) from FNs to participate in the consensus process. According to the Rivest-Shamir-Adleman (RSA) encryption algorithm applied to the blockchain system, FNs could verify the identity of the node through its public key to avoid malicious attacks. Additionally, to reduce the computation complexity of the consensus algorithms and the network overhead, we propose a dynamic offloading and resource allocation (DORA) algorithm and a reputation-based democratic byzantine fault tolerant (R-DBFT) algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security. Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead, and obtain a considerable performance improvement compared to the related algorithms in the previous literature.
{"title":"Joint optimization of energy consumption and network latency in blockchain-enabled fog computing networks","authors":"Xiaoge Huang, Hongbo Yin, Cao Bin, Yongsheng Wang, Qianbin Chen, Zhang Jie","doi":"10.23919/JCC.fa.2023-0488.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0488.202404","url":null,"abstract":"Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things (IoT) devices. To ensure the security of private data, in this paper, we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network. A reputation model is proposed to update the credibility of the fog nodes (FN), which is used to select blockchain nodes (BN) from FNs to participate in the consensus process. According to the Rivest-Shamir-Adleman (RSA) encryption algorithm applied to the blockchain system, FNs could verify the identity of the node through its public key to avoid malicious attacks. Additionally, to reduce the computation complexity of the consensus algorithms and the network overhead, we propose a dynamic offloading and resource allocation (DORA) algorithm and a reputation-based democratic byzantine fault tolerant (R-DBFT) algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security. Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead, and obtain a considerable performance improvement compared to the related algorithms in the previous literature.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140773670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0493.202404
Ma Yue, Ruiqian Ma, Lin Zhi, Weiwei Yang, Yueming Cai, Miao Chen, Wu Wen
In this paper, the covert age of information (CAoI), which characterizes the timeliness and covertness performance of communication, is first investigated in the short-packet covert communication with time modulated retrodirective array (TMRDA). Specifically, the TMRDA is designed to maximize the antenna gain in the target direction while the side lobe is sufficiently suppressed. On this basis, the covertness constraint and CAoI are derived in closed form. To facilitate the covert transmission design, the transmit power and block-length are jointly optimized to minimize the CAoI, which demonstrates the trade-off between covertness and timelessness. Our results illustrate that there exists an optimal block-length that yields the minimum CAoI, and the presented optimization results can achieve enhanced performance compared with the fixed block-length case. Additionally, we observe that smaller beam pointing error at Bob leads to improvements in CAoI.
{"title":"Age of information for short-packet covert communication with time modulated retrodirective array","authors":"Ma Yue, Ruiqian Ma, Lin Zhi, Weiwei Yang, Yueming Cai, Miao Chen, Wu Wen","doi":"10.23919/JCC.fa.2023-0493.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0493.202404","url":null,"abstract":"In this paper, the covert age of information (CAoI), which characterizes the timeliness and covertness performance of communication, is first investigated in the short-packet covert communication with time modulated retrodirective array (TMRDA). Specifically, the TMRDA is designed to maximize the antenna gain in the target direction while the side lobe is sufficiently suppressed. On this basis, the covertness constraint and CAoI are derived in closed form. To facilitate the covert transmission design, the transmit power and block-length are jointly optimized to minimize the CAoI, which demonstrates the trade-off between covertness and timelessness. Our results illustrate that there exists an optimal block-length that yields the minimum CAoI, and the presented optimization results can achieve enhanced performance compared with the fixed block-length case. Additionally, we observe that smaller beam pointing error at Bob leads to improvements in CAoI.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140757475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0474.202404
Hongchang Ke, Wang Hui, Hongbin Sun, Halvin Yang
Emerging mobile edge computing (MEC) is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment (MWE) with limited computational resources and energy. Due to the homogeneity of request tasks from one MWE during a long-term time period, it is vital to predeploy the particular service cachings required by the request tasks at the MEC server. In this paper, we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks. Furthermore, we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme (MBOMS) to minimize the long-term average weighted cost. The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution. Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.
{"title":"Deep reinforcement learning-based task offloading and service migrating policies in service caching-assisted mobile edge computing","authors":"Hongchang Ke, Wang Hui, Hongbin Sun, Halvin Yang","doi":"10.23919/JCC.fa.2023-0474.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0474.202404","url":null,"abstract":"Emerging mobile edge computing (MEC) is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment (MWE) with limited computational resources and energy. Due to the homogeneity of request tasks from one MWE during a long-term time period, it is vital to predeploy the particular service cachings required by the request tasks at the MEC server. In this paper, we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks. Furthermore, we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme (MBOMS) to minimize the long-term average weighted cost. The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution. Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140776456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.23919/JCC.fa.2023-0295.202402
Zhengyuan Liang, Junbin Liang, Guoxuan Zhong
The reliability of a network is an important indicator for maintaining communication and ensuring its stable operation. Therefore, the assessment of reliability in underlying interconnection networks has become an increasingly important research issue. However, at present, the reliability assessment of many interconnected networks is not yet accurate, which inevitably weakens their fault tolerance and diagnostic capabilities. To improve network reliability, researchers have proposed various methods and strategies for precise assessment. This paper introduces a novel family of interconnection networks called general matching composed networks (gMCNs), which is based on the common characteristics of network topology structure. After analyzing the topological properties of gMCNs, we establish a relationship between super connectivity and conditional diagnosability of gMCNs. Furthermore, we assess the reliability of gMCNs, and determine the conditional diagnosability of many interconnection networks.
{"title":"Reliability assessment of a new general matching composed network","authors":"Zhengyuan Liang, Junbin Liang, Guoxuan Zhong","doi":"10.23919/JCC.fa.2023-0295.202402","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0295.202402","url":null,"abstract":"The reliability of a network is an important indicator for maintaining communication and ensuring its stable operation. Therefore, the assessment of reliability in underlying interconnection networks has become an increasingly important research issue. However, at present, the reliability assessment of many interconnected networks is not yet accurate, which inevitably weakens their fault tolerance and diagnostic capabilities. To improve network reliability, researchers have proposed various methods and strategies for precise assessment. This paper introduces a novel family of interconnection networks called general matching composed networks (gMCNs), which is based on the common characteristics of network topology structure. After analyzing the topological properties of gMCNs, we establish a relationship between super connectivity and conditional diagnosability of gMCNs. Furthermore, we assess the reliability of gMCNs, and determine the conditional diagnosability of many interconnection networks.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.23919/JCC.ja.2023-0164
Wenjun Jiang, Zhihao Ou, Xiaojun Yuan, Li Wang
This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation (TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.
{"title":"Turbo message passing based burst interference cancellation for data detection in massive MIMO-OFDM systems","authors":"Wenjun Jiang, Zhihao Ou, Xiaojun Yuan, Li Wang","doi":"10.23919/JCC.ja.2023-0164","DOIUrl":"https://doi.org/10.23919/JCC.ja.2023-0164","url":null,"abstract":"This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation (TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140463326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.23919/JCC.fa.2022-0639.202402
P. Pabitha, Anusha Jayasimhan
Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation, instance segmentation, and many others. In image and video recognition applications, convolutional neural networks (CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output. Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.
{"title":"An efficient approach to escalate the speed of training convolution neural networks","authors":"P. Pabitha, Anusha Jayasimhan","doi":"10.23919/JCC.fa.2022-0639.202402","DOIUrl":"https://doi.org/10.23919/JCC.fa.2022-0639.202402","url":null,"abstract":"Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation, instance segmentation, and many others. In image and video recognition applications, convolutional neural networks (CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output. Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140464126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.23919/JCC.fa.2023-0313.202402
Haoran Xie, Y. Zhan, Jianhua Lu
With the development of the transportation industry, the effective guidance of aircraft in an emergency to prevent catastrophic accidents remains one of the top safety concerns. Undoubtedly, operational status data of the aircraft play an important role in the judgment and command of the Operational Control Center (OCC). However, how to transmit various operational status data from abnormal aircraft back to the OCC in an emergency is still an open problem. In this paper, we propose a novel Telemetry, Tracking, and Command (TT&C) architecture named Collaborative TT&C (CoTT&C) based on mega-constellation to solve such a problem. CoTT&C allows each satellite to help the abnormal aircraft by sharing TT&C resources when needed, realizing real-time and reliable aeronautical communication in an emergency. Specifically, we design a dynamic resource sharing mechanism for CoTT&C and model the mechanism as a single-leader-multi-follower Stackelberg game. Further, we give an unique Nash Equilibrium (NE) of the game as a closed form. Simulation results demonstrate that the proposed resource sharing mechanism is effective, incentive compatible, fair, and reciprocal. We hope that our findings can shed some light for future research on aeronautical communications in an emergency.
{"title":"Mega-constellations based TT&C resource sharing: Keep reliable aeronautical communication in an emergency","authors":"Haoran Xie, Y. Zhan, Jianhua Lu","doi":"10.23919/JCC.fa.2023-0313.202402","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0313.202402","url":null,"abstract":"With the development of the transportation industry, the effective guidance of aircraft in an emergency to prevent catastrophic accidents remains one of the top safety concerns. Undoubtedly, operational status data of the aircraft play an important role in the judgment and command of the Operational Control Center (OCC). However, how to transmit various operational status data from abnormal aircraft back to the OCC in an emergency is still an open problem. In this paper, we propose a novel Telemetry, Tracking, and Command (TT&C) architecture named Collaborative TT&C (CoTT&C) based on mega-constellation to solve such a problem. CoTT&C allows each satellite to help the abnormal aircraft by sharing TT&C resources when needed, realizing real-time and reliable aeronautical communication in an emergency. Specifically, we design a dynamic resource sharing mechanism for CoTT&C and model the mechanism as a single-leader-multi-follower Stackelberg game. Further, we give an unique Nash Equilibrium (NE) of the game as a closed form. Simulation results demonstrate that the proposed resource sharing mechanism is effective, incentive compatible, fair, and reciprocal. We hope that our findings can shed some light for future research on aeronautical communications in an emergency.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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